Three-dimensional (3D) representations of tissues can be readily understood by the human brain and are the most informative and accurate way to quantitatively and comprehensively study cellular state and its relationships in health and disease. To generate 3D tissue representations, molecular measurements at single cell resolution are needed. These measurements can be performed directly from intact tissue, or alternatively, serial sections of a tissue can be generated, measured and assembled into a 3D object. Equally important to data generation are powerful computational tools that enable first, integration of various data types with different resolutions into multiscale 3D tissue volumes; second, to identify single cells; and third, to derive meta-features from such 3D single cell tissue models. The computational tools employed and developed by the Data Analysis Core will not only enable such analyses of the lymphatic tissues, but will also be generally applicable to a wide range of molecular data types and tissues. Specifically, the Data Analysis Core will provide the infrastructure and computational tools to store the data and metadata, to integrate the different measurement modalities into multi-scale images, to generate 3D voxel representations of tissues, to identify the single cells in 3D representation, and to determine cell types, their neighborhood and other features. All of these analyses will be built on an open source computational pipeline (histoCAT), which was developed in the lab of Dr. Bodenmiller and is emerging as a standard analysis pipeline for highly multiplexed 2D and 3D tissue data of various types. The Data Analysis Core will use OME-tiff as a standard format for all data and metadata. Data will be stored in a flexible database (openBIS) that enables straightforward exchange of raw data, data at any step of processing, and the processing pipeline itself to the HIVE. The structure of data and metadata storage can be readily harmonized to the needs of the HIVE. Given that all molecular measurements in our proposal provide single cell resolved information, we will use the single cell as a ?bucket? to integrate different imaging modalities. The images generated by the optical microscopy methods and by imaging mass cytometry will be segmented, and using cell labels and cellular and tissue features, the different data modalities will be integrated to generate multiscale, multiparamter images. The multiscale, serial 2D tissue maps will be registered to build the 3D voxel tissue models. A single cell resolved model will be generated using 3D segmentation approaches. Many algorithms will then be employed to derive meta-features, such as cell shapes, patterns of cellular neighborhoods, distances to morphological features, and tissue motifs. These meta-features can be visualized on the 3D model to support the study of biological phenomena. The proposed computational pipeline, together with the unprecedented datasets generated of the lymphatic organs within this project will provide highest quality and comprehensive 3D Atlas of the lymphatic organs and a scalable blueprint of data processing and visualization that can be readily employed for other data types and tissues.

National Institute of Health (NIH)
National Institute of Allergy and Infectious Diseases (NIAID)
Specialized Center--Cooperative Agreements (U54)
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Special Emphasis Panel (ZRG1)
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University of Florida
United States
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